Beyond Keywords: Navigating the Future with Vector Search

If you’re reading this article, it is no coincidence. Why do we say so? Think about it, there are probably thousands of articles on the web and it is this one that you clicked on. Now, we are not trying to make you believe in the concept of destiny since of course there is the role that SEO plays. Now the way SEO works is by adding meaningful keywords in your content. But are you aware that this technique of using keywords is a thing of the past now? What’s trending now is Vector Search. Vector Search, what is that? In this article, we’re going to discuss exactly that. 

What is Vector Search?

Vector Search is a search algorithm used for modern applications to search for information that is needed at the moment. The reason why this upgraded search algorithm has been introduced in the market is that as new users on modern applications increase at an exponential rate it becomes difficult for organizations to deploy searching methodologies that retrieve required information at a fast rate. 

But why do we need a whole new search algorithm to do so? The answer to those questions can be acquired once you get to know the workings of the vector search algorithms and how exactly they revolutionalize the whole task of information retrieval. 

How does Vector Search work?

To understand how vector search works, you first need to know what exactly is a vector. Vector is a mathematical term representing data in a multi-dimensional space. These vectors are used to represent various types of data, such as text, images, or any other structured or unstructured information. Vector Search is an algorithm that searches for information in a vector database by mapping each data item to a vector representation of itself. The key innovation behind vector search lies in these vectors capturing not just the raw data but also the relationships and similarities between data items. 

Now, there are three major techniques that are used to implement vector search algorithms. Let’s have a look at them one by one.

Vector Embedding

The biggest concern for organizations was raised when data in various forms had to be stored in the company dataset and then they had to search for information in that huge complex database for one piece of information. Information retrieval in such databases can be made simpler if data can be stored in one form regardless of the type it belongs to. Such a database with data points in one fixed form will make it much easier and more efficient to carry out operations and computations on the database. In vector search, vector embedding is how one can do so. Vector embeddings are the numeric representation of data and related context stored in high dimensional (dense) vectors.

Similarity Score

If you are impressed by the concept of storing complex data points in one format by embedding them as vectors, wait a minute! There’s another great thing about Vector Search. Vector Search simplifies comparing two datasets by the use of a concept called the similarity score. The idea of similarity score is that if two data points are similar, their vector representation will also be similar. By indexing queries and documents with vector embeddings, you find similar documents as the nearest neighbors of your query.

ANN Algorithm

The ANN algorithm is yet another method to account for the similarity between two datasets. The reason why the ANN algorithm is efficient is because it sacrifices perfect accuracy in exchange for executing efficiently in high dimensional embedding spaces, at scale. This proves to be effective relative to the traditional nearest neighbor algorithms like the k-nearest neighbor algorithm (kNN) which leads to excessive execution times and zaps computational resources. 

Vector Search v/s Traditional Search

The traditional search algorithms that mainly focus on keywords are outdated and not really beneficial. For modern applications, there are hundreds of reasons why vector search is the algorithm that should be implemented to carry out operations. What are those reasons, you ask? We come prepared mate. Here’s a detailed differentiation between Vector Search and Traditional Search just for you:

Aspect Vector Search Traditional Search
Query Approach Semantic understanding of context and meaning Keyword-based with exact matching
Matching Technique Similarity matching between vectors String matching based on keywords
Context Awareness High, understands context and intent Limited, relies on specific keywords
Handling Ambiguity Handles polysemy and word ambiguity Vulnerable to keyword ambiguity
Data Types Versatile, works with various data types Primarily text-based search
Efficiency Efficient, suitable for large datasets May become less effective as data scales
Examples Content recommendation, image search Standard web search, database queries

 

Ethical and Privacy Concerns

While AI can be really helpful to achieve efficiency and accuracy, a proper probe is required to keep ethical activities in check. Vector Search algorithms have repeatedly been questioned for how they address privacy concerns. Recently, the CEO of OpenAI, Sam Altman suggested that it’s the right time now to appoint a committee that will be responsible for checking whether the AI practices being carried out are ethical are not. Ethical implications related to vector search involve privacy concerns and bias in results. Only when these ethical aspects are considered can we say that AI is actually “intelligent”. To do so, Best practices for addressing these ethical issues must be presented and implemented. 

Applications of Vector Search

Here are all those places where you’ll see the use of Vector Search to improve user experience. 

  1. Netflix: Ever wondered how Netflix knows literally every time what movies or shows you’d like to watch? Don’t worry they don’t have had a spy deployed at your home. It’s the Vector Search that does the magic. It considers the content of what you’ve watched and suggests similar titles.
  2. Spotify: The Same is the story for Spotify. Spotify employs vector search to suggest music tracks and playlists based on your listening history and preferences. It can recommend songs with similar musical characteristics to your favorite tracks.
  3. Ad Targeting: Advertisers use vector search to target ads to users based on their interests and online behavior, increasing the relevance of advertisements. Sales Representatives and marketers, take notes!

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